Abstract Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been
widely employed in medical image segmentation. While CNNs excel in local feature …

Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs)
have been two dominant architectures in medical image segmentation. Although CNNs can …

Medical image segmentation plays an essential role in developing computer-assisted
diagnosis and treatment systems, yet it still faces numerous challenges. In the past few …

Abstract Vision Transformer (ViT) has shown comparable capabilities to convolutional
neural networks for medical image segmentation in recent years. However, most ViT-based …

Medical image segmentation is an essential prerequisite for developing healthcare systems,
especially for disease diagnosis and treatment planning. On various medical image …

Recent advancements in medical image segmentation have demonstrated superior
performance by combining Transformer and U-Net due to the Transformer's exceptional …

Accurate segmentation of organs or lesions from medical images is crucial for reliable
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …

Biomedical image segmentation plays an essential role in developing computer-assisted
diagnosis and treatment systems, yet it still faces numerous challenges. In the past few …

The accurate segmentation of medical images is critical for various healthcare applications.
Convolutional neural networks (CNNs), especially Fully Convolutional Networks (FCNs) like …

In the past decades, deep neural networks, particularly convolutional neural networks, have
achieved state-of-the-art performance in a variety of medical image segmentation tasks …